R bugzilla review

Introduction

What is the bugRzilla Package?

BugRzilla is an R package that helps the user to interact with the Bugzilla through an API.

To learn more, see bugRzilla.

About the bugRzilla Google Summer of COde Project:-

bugRzilla is a package to interact with a bugzilla API and specially with R bugzilla. The goal of the project is to help users to submit issues to R Bugzilla.

About the This Project:-

Explore the issues and bugs on the R Bugzilla to make the submission from bugRzilla better. It might help to identify useful patterns for R core or report the status of the R Bugzilla.

To learn more, see bugzilla_viz.

Setup Database on your local system

Download SQL and MySQL Workbench

To install SQL on Ubuntu one can refer a blog post by digitalocean. To install MySQL workbench on Ubuntu one can refer a blog post by linuxhint

Download R_bugzilla data

1. The R_bugzilla data can be downloaded from link.
2. Since the downloaded data is a zip file so make sure you unzip the file by directly using extract here option to the folder you desire before dumping the file which will have an extension .sql (eg: R-bugs.sql).

Dump downloaded R_bugzilla to MySQL workbench.

Before one import the R_bugzilla SQL file one needs to create the (empty) database from MySQL if it doesn’t exist already and the exported SQL don’t contain CREATE DATABASE (exported with –no-create-db or -n option), before you can import it.

After considering this open your Terminal and run the command: mysqldump -u my_username -p database_name > output_file_path or you can use mysql using the command: source <Path>/R-bugs.sql;

The options in use are:

1. The -u flag indicates that the MySQL username will follow.
2. The -p flag indicates we should be prompted for the password associated with the above username. database_name is of course the exact name of the database to export. eg. bugRzilla is the empty database you created.
3. The > symbol is a Unix directive for STDOUT, which allows Unix commands to output the text results of the issued command to another location. In this case, that output location is a file path, specified by output_file_path.

For Example,

1. At the command prompt, run the following command to launch the mysql shell and enter it as the root user: mysql -u root -p
2. When you’re prompted for a password, enter the one that you set at installation time, or if you haven’t set one, press Enter to submit no password. The following mysql shell prompt should appear: mysql>
3. In MySQL, I used this to dump the data in the empty database:
   • Create an empty database: create database bugRzilla;
   • To check whether the database is created or not use: show databases;
   • Once an empty database is created then to dump the SQL data in the database use: source /home/data/Documents/GSOC/R-bugs.sql;

   • To check your database is dumped correctly use: show tables;

     mysql> show tables;
     +---------------------+
     | Tables_in_bugRzilla |
     +---------------------+
     | attachments         |
     | bugs                |
     | bugs_activity       |
     | bugs_fulltext       |
     | bugs_mod            |
     | components          |
     | longdescs           |
     +---------------------+
     7 rows in set (0.00 sec)

bugRzilla Analysis

For the connection to the database, I’m using the dplyr package, it supports to the widely-used open source databases like MySQL.

The libraries used for the analysis:

# loading packages
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(dbplyr)

## 
## Attaching package: 'dbplyr'

## The following objects are masked from 'package:dplyr':
## 
##     ident, sql

library(RMySQL)

## Loading required package: DBI

library(DBI)
library(DT)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ stringr 1.4.0
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ readr   1.4.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dbplyr::ident() masks dplyr::ident()
## x dplyr::lag()    masks stats::lag()
## x dbplyr::sql()   masks dplyr::sql()

library(skimr)
library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Connect bugRzilla SQL Database with R

# Connecting R with MySQL
con <- dbConnect(
    MySQL(),
    dbname='bugRzilla', # change the database name to your database name
    username='root', # change the username to your username
    password='1204', # update your password
    host='localhost',
    port=3306)

#  Accessing Tables names from the Database
DBI::dbListTables(con)

## [1] "attachments"   "bugs"          "bugs_activity" "bugs_fulltext"
## [5] "bugs_mod"      "components"    "longdescs"

Data Exploartion of Bugs Table from the Database

bugs_df <- tbl(con, "bugs")

## Warning in .local(conn, statement, ...): Decimal MySQL column 24 imported as
## numeric

## Warning in .local(conn, statement, ...): Decimal MySQL column 25 imported as
## numeric

#for quick view of the datatypes and the structure of data
skim(bugs_df)

## Warning in .local(conn, statement, ...): Decimal MySQL column 24 imported as
## numeric

## Warning in .local(conn, statement, ...): Decimal MySQL column 25 imported as
## numeric

Data summary

Name	bugs_df
Number of rows	7042
Number of columns	27
_______________________
Column type frequency:
character	15
numeric	12
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
bug_file_loc	0	1	0	136	6990	51	0
bug_severity	0	1	5	11	0	7	0
bug_status	0	1	3	11	0	8	0
creation_ts	0	1	19	19	0	7028	0
delta_ts	0	1	19	19	0	6308	0
short_desc	0	1	1	255	0	6923	0
op_sys	0	1	3	15	0	22	0
priority	0	1	2	2	0	5	0
rep_platform	0	1	3	25	0	7	0
version	0	1	3	15	0	43	0
resolution	0	1	0	19	564	12	0
target_milestone	0	1	3	3	0	1	0
status_whiteboard	0	1	0	0	7042	1	0
lastdiffed	0	1	19	19	0	6324	0
deadline	7008	0	19	19	0	30	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
bug_id	0	1	10817.89	6189.36	1	5686.75	14101.5	16048.75	18097	▃▁▂▂▇
assigned_to	0	1	17.48	120.26	1	2.00	5.0	16.00	2787	▇▁▁▁▁
product_id	0	1	2.00	0.00	2	2.00	2.0	2.00	2	▁▁▇▁▁
reporter	0	1	685.69	1003.34	1	2.00	2.0	1056.00	3432	▇▂▁▁▁
component_id	0	1	9.84	5.20	2	6.00	9.0	15.00	19	▇▇▆▃▆
qa_contact	7042	0	NaN	NA	NA	NA	NA	NA	NA
votes	0	1	0.00	0.00	0	0.00	0.0	0.00	0	▁▁▇▁▁
everconfirmed	0	1	0.83	0.38	0	1.00	1.0	1.00	1	▂▁▁▁▇
reporter_accessible	0	1	1.00	0.00	1	1.00	1.0	1.00	1	▁▁▇▁▁
cclist_accessible	0	1	1.00	0.00	1	1.00	1.0	1.00	1	▁▁▇▁▁
estimated_time	0	1	0.10	6.60	0	0.00	0.0	0.00	552	▇▁▁▁▁
remaining_time	0	1	0.00	0.00	0	0.00	0.0	0.00	0	▁▁▇▁▁

From the above table we can conclude that the few of the columns are having wrong datatype like:

1. creation_ts
2. delta_ts
3. lastdiffed
4. estimated_time
5. remaining_time
6. deadline

Note:The Column estimated_time and remaining_time only contains the integer value. So, It can’t be transformed to Date format datatype. Also there are columns which are empty so they are of no use of the analysis like:

1. target_milestone
2. qa_contact
3. status_whiteboard

# Converting `bugs_df` to `dataframe`
bugs_df <- as.data.frame(bugs_df)

## Warning in .local(conn, statement, ...): Decimal MySQL column 24 imported as
## numeric

## Warning in .local(conn, statement, ...): Decimal MySQL column 25 imported as
## numeric

Cleaning the data

First steps, check the data and prepare it for what we want:

#converting the required fields in the correct datatype format
bugs_df <- bugs_df %>%
    mutate_at(vars("creation_ts", "delta_ts", "lastdiffed", "deadline"), as.Date)
# Taking the columns which are useful
bugs_df <- bugs_df %>%
    select("bug_id", "bug_severity", "bug_status", "creation_ts", "delta_ts", "op_sys", "priority", "resolution", "component_id", "version", "lastdiffed", "deadline")
#for quick view of the datatypes and the structure of data
skim(bugs_df)

Data summary

Name	bugs_df
Number of rows	7042
Number of columns	12
_______________________
Column type frequency:
character	6
Date	4
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
bug_severity	0	1	5	11	0	7	0
bug_status	0	1	3	11	0	8	0
op_sys	0	1	3	15	0	22	0
priority	0	1	2	2	0	5	0
resolution	0	1	0	19	564	12	0
version	0	1	3	15	0	43	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
creation_ts	14	1	1998-08-07	2021-05-07	2009-12-08	4274
delta_ts	30	1	1998-08-09	2021-05-08	2012-07-20	3562
lastdiffed	14	1	1998-08-07	2021-05-08	2012-07-10	3565
deadline	7008	0	2010-04-23	2015-04-23	2013-11-09	30

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
bug_id	0	1	10817.89	6189.36	1	5686.75	14101.5	16048.75	18097	▃▁▂▂▇
component_id	0	1	9.84	5.20	2	6.00	9.0	15.00	19	▇▇▆▃▆

#showing the `datatable`
datatable(head(bugs_df, 5), options = list(scrollX = TRUE))

About the Bugs Data used for Analysis

I’ve taken the 12 columns under consideration to Analyse the Data. The brief description about the columns as follows:

1. bug_id: Unique numeric identifier for bug.
2. bug_severity: How severe the bug is, e.g. enhancement, critical, etc.
3. bug_status: Current status, e.g. NEW, RESOLVED, etc.
4. creation_ts: When bug was filed.
5. delta_ts: The timestamp of the last update on the bug. This includes updates to some related tables (e.g. “longdescs”).
6. op_sys: Operating system bug was seen on, e.g. Windows Vista, Linux, etc.
7. priority: The priority of the bug (P1 = most urgent, P5 = least urgent).
8. resolution: The resolution, if the bug is in a closed state, e.g. FIXED, DUPLICATE, etc.
9. component_id: Numeric ids of the components.
10. version: Version of software in which bug is seen.
11. lastdiffed: The time at which information about this bug changing was last emailed to the cc list.
12. deadline: Date by which bug must be fixed.

Visualizations

bug_created <- bugs_df %>%
    ggplot(aes(x = creation_ts, y = bug_id)) +
    geom_line(color = "darkorchid4") +
    labs(title = "Bug Creation",
           subtitle = "The data frame is sent to the plot using pipes",
           y = "Bug ID",
           x = "Date") +
    theme_bw(base_size = 15)
ggplotly(bug_created)

The Time-series graph shows that which bug_id was filed in which month and year and from the bar graph we can conclude that in which year the most bugs are filed and when one will zoom the graphs, one can see on which date which bug was filed. The most of the Bugs are filled in the month of January and July.

last_modified <- bugs_df %>%
    ggplot(aes(x = lastdiffed, y = bug_id)) +
    geom_line() +
    labs(title = "Bug Last Modified",
         y = "Bug ID",
         x = "Date") +
    theme_bw(base_size = 15)
ggplotly(last_modified)

The Time-series graph shows that which bug_id was the last update. Most of the bugs are last updated in the month of January, March, April, and July and in the year from 2014 to 2016 most bugs are modified and in 2019 to 2020 most bugs are filed.

# Plotting the Time Series graph with the bug_id and delta_ts
last_modified_graph <- bugs_df %>%
    ggplot(aes(x = delta_ts, y = bug_id)) +
    geom_point() +
    labs(title = "Bug changing was last emailed to the cc list",
           y = "Bug ID",
           x = "Date") + theme_bw(base_size = 15)
ggplotly(last_modified_graph)

The Time-series graph shows that which bug_id was the last update. Most of the bugs are last updated in the month of January,March, April, and July.

Resolution_graph <- ggplot(bugs_df,aes(x = resolution)) +
    geom_bar() +
    scale_x_discrete(guide = guide_axis(n.dodge = 5)) +
    labs(
        title = "Bug Resolution Bar graph with Bug Count",
        x = "Resolution",
        y = "Bug Count"
    ) + coord_flip()
ggplotly(Resolution_graph)

The Resolution bar-graph shows which bug_id belongs to which resolution category, if the bug is in a closed state, e.g. FIXED, DUPLICATE, etc. As we can conclude, that most bugs belong to the fixed category of the resolution.

Status_graph <- ggplot(bugs_df,aes(x = bug_status)) +
    geom_bar() +
    scale_x_discrete(guide = guide_axis(n.dodge = 5)) +
    labs(
        title = "Bug Status Bar graph with Bug Count",
        x = "Bug Status",
        y = "Bug Count"
    )
ggplotly(Status_graph)

The bug_status bar-graph shows which bug_id belongs to which category of bug_status, e.g. NEW, RESOLVED, etc. As we can conclude, that most bugs belong to the closed category of the bug_status.

Severity_graph <- ggplot(bugs_df,aes(x = bug_severity)) +
    geom_bar() +
    scale_x_discrete(guide = guide_axis(n.dodge = 5)) +
    labs(
        title = "Bug Severity Bar graph with Bug Count",
        x = "Bug Severity",
        y = "Bug Count"
    )
ggplotly(Severity_graph)

The bug_severity bar-graph shows which bug_id belongs to which category of bug_severity. Most of the bugs which are filed are normal, some of the bugs that are filled under enhancements are retested for some minor and major features, and a very few bugs are filed under the blocker category.

Data Exploartion of bugs and Attachments Table from the Database

bugs_attach_df <- tbl(con, "attachments")
# Converting `bugs_attach_df` to `dataframe`
bugs_attach_df <- as.data.frame(bugs_attach_df)
#for quick view of the datatypes and the structure of data
skim(bugs_attach_df)

Data summary

Name	bugs_attach_df
Number of rows	1823
Number of columns	11
_______________________
Column type frequency:
character	5
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
creation_ts	0	1	19	19	0	1771	0
modification_time	0	1	19	19	0	1630	0
description	0	1	0	174	187	1485	0
mimetype	0	1	8	71	0	69	0
filename	0	1	3	70	0	1522	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
attach_id	0	1	1876.50	572.77	1	1362.5	1895	2380.5	2838	▁▃▇▇▇
bug_id	0	1	15351.15	3661.05	1	15004.0	16413	17369.0	18097	▁▁▁▁▇
ispatch	0	1	0.43	0.49	0	0.0	0	1.0	1	▇▁▁▁▆
submitter_id	0	1	1313.33	1104.28	1	317.0	979	2143.0	3432	▇▆▂▃▃
isobsolete	0	1	0.12	0.32	0	0.0	0	0.0	1	▇▁▁▁▁
isprivate	0	1	0.00	0.00	0	0.0	0	0.0	0	▁▁▇▁▁

Cleaning attachments Data

bugs_attach_df <- bugs_attach_df %>%
    mutate_at(vars("creation_ts", "modification_time"), as.Date) %>%
    mutate_at(vars("isobsolete", "isprivate", "ispatch"), as.logical)

Joining the bugs and attachments tables

#joining the `attachments` and `bugs` table
baa <- merge(bugs_attach_df, bugs_df, by = intersect(names(bugs_attach_df), names(bugs_df)), all = TRUE)

# Created four columns `creation_month`, `creation_year` and `lastdiffed_month`, `lastdiffed_year` to find in which month and year a bug is created and modified respectively.
baa <- baa %>%
    mutate(creation_month = format(creation_ts, "%m"), 
           creation_year = format(creation_ts, "%Y"), 
           lastdiffed_month = format(lastdiffed, "%m"), 
           lastdiffed_year = format(lastdiffed, "%Y")) %>%
    group_by(creation_month, creation_year)

#showing the `datatable`
datatable(head(baa, 5), options = list(scrollX = TRUE))

About the bugs_activity and attachments Data Used for Analysis

I’ve taken the 15 columns under consideration to Analyse the Data. The brief description about the columns as follows:

1. bug_id: Unique numeric identifier for bug.
2. attach_id: Unique numeric identifier for attachment.
3. creation_ts: When bug was filed.
4. modification_time: The date and time on which the attachment was last modified.
5. description: Text describing the attachment.
6. mimetype: Content type of the attachment like text/plain or image/png.
7. ispatch: Whether attachment is a patch.
8. filename :Path-less file-name of attachment.
9. submitter_id: Unique numeric identifier for who submitted the bug.
10. isobsolete: Whether attachment is marked obsolete.
11. isprivate: TRUE if the attachment should be private and FALSE if the attachment should be public.
12. creation_month: The month in which the bug is created.
13. creation_year: The year in which the bug is created.
14. lastdiffed_month: The month in which the bug is last modified.
15. lastdiffed_year: The year in which the bug is last modified.

Visualizations

#Counting number of bugs per month in an year
bugs_counts <- baa %>%
    arrange(bug_id) %>%
    count(creation_year)
skim(head(bugs_counts))

Data summary

Name	head(bugs_counts)
Number of rows	6
Number of columns	3
_______________________
Column type frequency:
numeric	1
________________________
Group variables	creation_month, creation_year

Variable type: numeric

skim_variable	creation_month	creation_year	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
n	01	1999	0	1	17	NA	17	17	17	17	17	▁▁▇▁▁
n	01	2000	0	1	13	NA	13	13	13	13	13	▁▁▇▁▁
n	01	2001	0	1	30	NA	30	30	30	30	30	▁▁▇▁▁
n	01	2002	0	1	41	NA	41	41	41	41	41	▁▁▇▁▁
n	01	2003	0	1	30	NA	30	30	30	30	30	▁▁▇▁▁
n	01	2004	0	1	21	NA	21	21	21	21	21	▁▁▇▁▁

Note: Here only I’ve shown the overview of only the 6 rows of bugs_counts Since the whole summary of the data is very large.

# 3D plot to see the number of bug counts per month in a year
bug_count_graph <- plot_ly(
    x = bugs_counts$creation_month,
    y = bugs_counts$creation_year,
    z = bugs_counts$n,
    type="scatter3d", 
    mode="markers", marker = list(size=2)) 

bug_count_graph <- bug_count_graph %>%
    layout(
        title = "Bug Counts per month in a year"
    )
bug_count_graph

## Warning: Ignoring 1 observations

The 3D graph or surface plot is about the number of bugs counts per month in a year. The Most number of bug count is 77 in April 2015 and the minimum bug_count is 2.

#filtering the data where resolution is Duplicate
res_dupli <- baa %>%
    filter(resolution == "DUPLICATE" & bug_status == "CLOSED")

# plotting graph with creation month where resolution is Duplicate
dupli_month_graph <- ggplot(res_dupli) +
    geom_bar(aes(x = creation_month)) +
    labs(
        title = "Months in which Duplicate Bugs are Filed",
        x = "Months",
        y = "Bug_Count"
    )
ggplotly(dupli_month_graph)

The above bar graph is about the month in which is bugs are filed where resolution is Duplicate. From the graph, we can see that most were filled in the month of August with a bug count of 12 and the least bugs are filled in the month of December with a bug count of 2. This graph is from the year 2006 to 2021.

# plotting graph with creation year where resolution is Duplicate
duplicate_year <- ggplot(res_dupli) +
    geom_bar(aes(x = creation_year)) +
    labs(
        title = "Year in which Duplicate Bugs are Filed",
        x = "Year",
        y = "Bug_Count"
    )
ggplotly(duplicate_year)

The above Visualization is about the year in which is bugs are filed where resolution is Duplicate. From the graph, we can see that the most ware filled in the year 2012 having a bug count of 11 and the least bugs are filled in the year 2007, 2008 and 2019 having a bug count of 1. This graph is from the year 2006 to 2021.

#filtering the data where resolution is Fixed
res_fixed <- baa %>%
    filter(resolution == "FIXED" & bug_status == "CLOSED")

# plotting graph with last modified year where resolution is Fixed
fixed_year_graph <- ggplot(res_fixed) +
    geom_bar(aes(x = lastdiffed_year)) +
    labs(
        title = "Year in which fixed bugs are last modified",
        x = "Year",
        y = "Bug_Count"
    ) +
    coord_flip()
ggplotly(fixed_year_graph)

The above Visualization is about the year in which is bugs are last modified where resolution is Fixed and their status is closed. From the graph, we can see that the most wast last modified in the year 2002 having a bug count of 328 and In the year, 2021 47 bugs are fixed and closed.

# plotting graph with creation year where resolution is Fixed
fixed_month_graph <- ggplot(res_fixed) +
    geom_bar(aes(x = lastdiffed_month)) +
    labs(
        title = "Month in which fixed bugs are last modified",
        x = "Month ",
        y = "Bug_Count"
    )
ggplotly(fixed_month_graph)

The bar graph is about the month in which is bugs are last modified where the resolution is Fixed. From the graph, we can see that the most wast last modified in the month December having a bug count of 559 and in the month of September having a bug count of 228 are least modified. This graph is from the year 1998 to 2021.

res_invalid <- baa %>%
    filter(resolution == "INVALID" & bug_status == "CLOSED")

invalid_graph <- ggplot(res_invalid) +
    geom_bar(aes(x = creation_month)) +
    labs(
        title = "Month in which invalid bugs are filed",
        x = "Month ",
        y = "Bug_Count"
    )
ggplotly(invalid_graph)

The bar graph is about the month in which is bugs are last modified where resolution is Invalid and status is closed. From the graph, we can see that the most were created in the month October having a bug count of 625 and in the month of February having a bug count of 491 are created. This graph is from the year 1998 to 2021.

invaild_year_graph <- ggplot(res_invalid) +
    geom_bar(aes(x = creation_year)) +
    labs(
        title = "Year in which INVALID Bugs are Filed",
        x = "Year",
        y = "Bug_Count"
    ) + coord_flip()
ggplotly(invaild_year_graph)

The bar graph refers to the Creation of Invalid bugs. In the year, 1998 the total of 63 Invalid bugs are created which are least, and in the year 2013 a total of 431 bugs are filed which are most.

priority_graph <- baa %>%
    ggplot(aes(x = creation_year, y = bug_id)) +
    geom_point() +
    facet_wrap( ~priority) +
    labs(title = "Bugs created year with their priorities",
         y = "Bug ID",
         x = "Date") + theme_bw(base_size = 9) +
    coord_flip()
ggplotly(priority_graph)

The Lattice plot gives insight about the bugs when they are created and under which priority they fall like from the above plot we can conclude that the majority of the bugs are filed under the P5 which is having the least priority.

Data Exploration of all the tables in the database

Data Exploartion of bugs_mod Table from the Database

bugs_mod_df <- tbl(con, "bugs_mod")
# Converting `bugs_mod_df to `dataframe`
bugs_mod_df <- as.data.frame(bugs_mod_df)
#for quick view of the datatypes and the structure of data
skim(bugs_mod_df)

Data summary

Name	bugs_mod_df
Number of rows	7042
Number of columns	28
_______________________
Column type frequency:
character	18
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
row_names	0	1.00	1	4	0	7042	0
bug_file_loc	7042	0.00	NA	NA	0	0	0
bug_severity	0	1.00	5	11	0	7	0
bug_status	0	1.00	3	11	0	8	0
creation_ts	14	1.00	10	10	0	4274	0
delta_ts	7042	0.00	NA	NA	0	0	0
short_desc	0	1.00	1	255	0	6923	0
op_sys	7042	0.00	NA	NA	0	0	0
priority	0	1.00	2	2	0	5	0
rep_platform	0	1.00	3	25	0	7	0
version	0	1.00	3	15	0	43	0
resolution	564	0.92	4	19	0	11	0
target_milestone	0	1.00	3	3	0	1	0
status_whiteboard	7042	0.00	NA	NA	0	0	0
lastdiffed	7042	0.00	NA	NA	0	0	0
estimated_time	0	1.00	4	6	0	19	0
remaining_time	0	1.00	4	4	0	1	0
deadline	7008	0.00	10	10	0	30	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
bug_id	0	1	10817.89	6189.36	1	5686.75	14101.5	16048.75	18097	▃▁▂▂▇
assigned_to	0	1	17.48	120.26	1	2.00	5.0	16.00	2787	▇▁▁▁▁
product_id	0	1	2.00	0.00	2	2.00	2.0	2.00	2	▁▁▇▁▁
reporter	0	1	685.69	1003.34	1	2.00	2.0	1056.00	3432	▇▂▁▁▁
component_id	0	1	9.84	5.20	2	6.00	9.0	15.00	19	▇▇▆▃▆
qa_contact	7042	0	NaN	NA	NA	NA	NA	NA	NA
votes	0	1	0.00	0.00	0	0.00	0.0	0.00	0	▁▁▇▁▁
everconfirmed	0	1	0.83	0.38	0	1.00	1.0	1.00	1	▂▁▁▁▇
reporter_accessible	0	1	1.00	0.00	1	1.00	1.0	1.00	1	▁▁▇▁▁
cclist_accessible	0	1	1.00	0.00	1	1.00	1.0	1.00	1	▁▁▇▁▁

#showing the baa i.e `bugs_mod_df` table in the `datatable`
datatable(head(bugs_mod_df, 5), options = list(scrollX = TRUE))

Data Exploartion of longdescs Table from the Database

longdescs_df <- tbl(con, "longdescs")

## Warning in .local(conn, statement, ...): Decimal MySQL column 4 imported as
## numeric

# Converting `longdescs_df` to `dataframe`
longdescs_df <- as.data.frame(longdescs_df)

## Warning in .local(conn, statement, ...): Decimal MySQL column 4 imported as
## numeric

#for quick view of the datatypes and the structure of data
skim(longdescs_df)

Data summary

Name	longdescs_df
Number of rows	26942
Number of columns	11
_______________________
Column type frequency:
character	3
numeric	8
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
bug_when	0	1.00	19	19	0	26270	0
thetext	0	1.00	0	422285	772	25588	0
extra_data	24966	0.07	1	5	0	1948	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
comment_id	0	1	83378.70	7986.99	1	76528.25	83263.5	90215.75	97284	▁▁▁▃▇
bug_id	0	1	10479.44	6260.77	1	4195.00	13361.0	16072.00	18097	▅▁▃▂▇
who	0	1	457.47	896.85	1	2.00	2.0	412.00	3432	▇▁▁▁▁
work_time	0	1	0.00	0.04	0	0.00	0.0	0.00	5	▇▁▁▁▁
isprivate	0	1	0.00	0.00	0	0.00	0.0	0.00	0	▁▁▇▁▁
already_wrapped	0	1	0.00	0.00	0	0.00	0.0	0.00	0	▁▁▇▁▁
type	0	1	0.35	1.26	0	0.00	0.0	0.00	6	▇▁▁▁▁
is_markdown	0	1	0.04	0.20	0	0.00	0.0	0.00	1	▇▁▁▁▁

#showing the baa i.e `longdescs_df` table in the `datatable`
datatable(head(longdescs_df, 5), options = list(scrollX = TRUE))

Data Exploartion of bugs_activity Table from the Database

bugs_act_df <- tbl(con, "bugs_activity")
# Converting `longdescs_df` to `dataframe`
bugs_act_df <- as.data.frame(bugs_act_df)
#for quick view of the datatypes and the structure of data
skim(bugs_act_df)

Data summary

Name	bugs_act_df
Number of rows	15114
Number of columns	9
_______________________
Column type frequency:
character	3
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
bug_when	0	1	19	19	0	7290	0
added	0	1	0	133	142	400	0
removed	0	1	0	122	7656	374	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
bug_id	0	1.00	15286.06	2884.99	1	14762.25	15738.0	16894.00	18097	▁▁▁▁▇
attach_id	14880	0.02	2053.50	509.98	1	1711.25	2128.0	2420.50	2833	▁▁▅▇▇
who	0	1.00	462.89	892.91	1	6.00	18.0	308.00	3432	▇▁▁▁▁
fieldid	0	1.00	14.02	6.83	2	9.00	12.0	20.00	54	▇▃▁▁▁
comment_id	15086	0.00	92539.75	1993.95	89370	90952.75	91992.5	93529.25	96991	▆▇▅▂▂
id	0	1.00	8566.00	5187.40	1	3791.25	8723.0	13347.75	17143	▇▆▇▆▇

Joing all the the data tables

#joining all the data tables
total_data <- merge(bugs_df, bugs_act_df, by = intersect(names(bugs_df), 
                                                         names(bugs_act_df)), all = TRUE) %>%
    merge(., bugs_attach_df, by = intersect(names(.), 
                                            names(bugs_attach_df)), all = TRUE) %>%
    merge(., bugs_mod_df, by = intersect(names(.), 
                                         names(bugs_mod_df)), all = TRUE)
# creating a creation_year column 
total_data$creation_year <- as.Date(cut(total_data$creation_ts, 
                                         breaks = "year"))
# creating a creation_month column
total_data$creation_month <- as.Date(cut(total_data$creation_ts, 
                                         breaks = "month"))
# creating a creation_week column
total_data$creation_week <- as.Date(cut(total_data$creation_ts, 
                                        breaks = "week", start.on.monday = FALSE))

# creating a lastdiffed_year column
total_data$lastdiffed_year <- as.Date(cut(total_data$lastdiffed, 
                                           breaks = "year"))
# creating a lastdiffed_year column
total_data$lastdiffed_month <- as.Date(cut(total_data$lastdiffed, 
                                           breaks = "month"))
# creating a lastdiffed_year column
total_data$lastdiffed_week <- as.Date(cut(total_data$lastdiffed, 
                                          breaks = "week", start.on.monday = FALSE))

# selecting required columns for the analysis
total_data <- total_data %>%
    select("bug_id", "creation_ts", "bug_severity", "bug_status", "delta_ts", "op_sys", "priority", "resolution", 
           "component_id", "version", "lastdiffed", "deadline", "attach_id", "who", "bug_when", "fieldid", "added", 
           "removed", "modification_time", "description", "mimetype", "ispatch", "filename", "submitter_id", 
           "isobsolete", "isprivate", "assigned_to", "product_id", "reporter", "creation_year", "creation_month", 
           "creation_week", "lastdiffed_year", "lastdiffed_month", "lastdiffed_week")

#counting total_creation_bug_count
total_creation_bug_count <- total_data %>%
    arrange(creation_year) %>%
    count(bug_id)

# joining total_data and total_bug_count
total_data <- merge(total_creation_bug_count, total_data, by = intersect(names(total_creation_bug_count), 
                                                         names(total_data)), all = TRUE)

#for quick view of the datatypes and the structure of data
skim(total_data)

Data summary

Name	total_data
Number of rows	27199
Number of columns	36
_______________________
Column type frequency:
character	12
Date	11
logical	3
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
bug_severity	1697	0.94	5	11	0	7	0
bug_status	1697	0.94	3	11	0	8	0
op_sys	8739	0.68	3	15	0	22	0
priority	1697	0.94	2	2	0	5	0
resolution	2261	0.92	0	19	1251	12	0
version	1697	0.94	3	15	0	43	0
bug_when	12085	0.56	19	19	0	7290	0
added	12085	0.56	0	133	142	400	0
removed	12085	0.56	0	122	7656	374	0
description	25373	0.07	0	174	187	1485	0
mimetype	25373	0.07	8	71	0	69	0
filename	25373	0.07	3	70	0	1522	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
creation_ts	30	1.00	1998-08-07	2021-05-07	2012-12-31	4443
delta_ts	8770	0.68	1998-08-09	2021-05-08	2015-12-14	3562
lastdiffed	8753	0.68	1998-08-07	2021-05-08	2015-12-14	3565
deadline	27026	0.01	2010-04-23	2015-04-23	2013-11-08	30
modification_time	25375	0.07	1998-12-04	2021-05-07	2015-08-23	1093
creation_year	30	1.00	1998-01-01	2021-01-01	2012-01-01	24
creation_month	30	1.00	1998-08-01	2021-05-01	2012-12-01	274
creation_week	30	1.00	1998-08-02	2021-05-02	2012-12-30	1166
lastdiffed_year	8753	0.68	1998-01-01	2021-01-01	2015-01-01	24
lastdiffed_month	8753	0.68	1998-08-01	2021-05-01	2015-12-01	272
lastdiffed_week	8753	0.68	1998-08-02	2021-05-02	2015-12-13	1127

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
ispatch	25373	0.07	0.43	FAL: 1045, TRU: 781
isobsolete	25373	0.07	0.12	FAL: 1615, TRU: 211
isprivate	25373	0.07	0.00	FAL: 1826

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
bug_id	0	1.00	12992.23	5390.83	1	10414.0	15152.0	16615.5	18097	▂▁▁▁▇
n	0	1.00	5.65	4.40	2	3.0	5.0	7.0	51	▇▁▁▁▁
component_id	1697	0.94	9.82	5.30	2	6.0	9.0	15.0	19	▇▇▆▃▇
attach_id	25268	0.07	1887.74	569.83	1	1378.5	1920.0	2380.5	2838	▁▃▇▇▇
who	12085	0.56	462.89	892.91	1	6.0	18.0	308.0	3432	▇▁▁▁▁
fieldid	12085	0.56	14.02	6.83	2	9.0	12.0	20.0	54	▇▃▁▁▁
submitter_id	25373	0.07	1314.49	1104.91	1	317.0	983.5	2148.0	3432	▇▆▂▃▃
assigned_to	20157	0.26	17.48	120.26	1	2.0	5.0	16.0	2787	▇▁▁▁▁
product_id	20157	0.26	2.00	0.00	2	2.0	2.0	2.0	2	▁▁▇▁▁
reporter	20157	0.26	685.69	1003.34	1	2.0	2.0	1056.0	3432	▇▂▁▁▁

datatable(head(total_data, 13), options = list(scrollX = TRUE))

Visualizations

cre_last_year_graph <- total_data %>%
    ggplot(aes(y = n)) +
    geom_line(aes(x = creation_year)) +
    geom_line(aes(x = lastdiffed_year), color="steelblue", linetype="twodash") +
    labs(
        title = "Year in which bugs are Created vs Last modified",
        x = "year",
        y = "Bug_Count"
    )

ggplotly(cre_last_year_graph)

In the year 2015 and 2016, a total of 51 bugs are created and in the year 2020 a total of 51 bugs are modified as well as in the year 2015, 31 bugs are modified. From the year 2013 to 2016 there is a peak in the creation and modification of the bugs and from the year 2003 to 2009 the is an almost linear downfall and rising in the creation and modification of the bugs.

cre_last_month_graph <- total_data %>%
    ggplot(aes(y = n)) +
    geom_line(aes(x = creation_month)) +
    geom_line(aes(x = lastdiffed_month), color="steelblue", linetype="twodash") +
    labs(
        title = "Month in which are Created vs Last modified",
        x = "Month",
        y = "Bug_Count"
    )

ggplotly(cre_last_month_graph)

In the month December 2015 and January 2016, a total of 51 bugs are created which is a sudden increase. In the September 2016 to October 2016 and October 2016 to November 2016 there is a linear increase in the creation of the bugs. From the last_modified graph, in the November 2020 a total of 51 bugs are last modified mostly in every month an avg of 2 bugs are modified from till 2010 but from the 2011 there is an increase in the modification of the bugs having an avg of 9 bugs.

cre_last_week_graph <- total_data %>%
    ggplot(aes(y = n)) +
    geom_line(aes(x = creation_week)) +
    geom_line(aes(x = lastdiffed_week), color="steelblue", linetype="twodash") +
    labs(
        title = "Week in which bugs are Created vs Last modified",
        x = "Week",
        y = "Bug_Count"
    )

ggplotly(cre_last_week_graph)

In the December 2015 and January 2016, a total of 51 bugs are created per week which is a sudden increase. In the September 2016 to October 2016 and October 2016 to November 2016 there is a linear increase in the creation of the bugs. From the last_modified graph, in the November 2020 a total of 51 bugs are last modified mostly in every week an avg of 2 or 3 bugs are modified from till 2010 but from the 2011 there is an increase in the modification of the bugs having an avg of 10 to 12 bugs each week.

Conclusion

In this project, I’ve visualized the bugRzilla database creating various visualization. The major graphs are the time-series graph which is the relationship of bug_id, creation_ts, delta_ts, and last_modified to show when the bug was created, last modified & last email to the cc list, last modified respectively. Some time-series graphs can help us to see in which week, month, the year most bugs are created.

The bar graph which is the relationship between the bug_count, bug_status, bug_severity, resolution to check the status like closed, new, etc, how severe the bug is like whether the bug is critical, major, etc and the resolution of the bug-like if the bug is in a closed state, e.g. FIXED, DUPLICATE, etc.

If your Bug report is effective, then its chances to get fixed are higher. It totally depends upon the how effectively one report it. Like there are some values which are left and that should not be null in order to report the bugs effectively. For example the, the Priority, bug severity even the resolution of the bugs should be specified. From the bugs data table we can see that the column bug_file_loc is mostly empty like their should be proper reporting where the bug occurred. Event Priority is generally set from P1 to P5. P1 as “fix the bug with the highest priority” and P5 as ” Fix when time permits” There are many rows where the priority is not mentioned which in turns takes more time by the developer to set the priority. At last in the description of the bug these points should be there which can create the report much effective i.e. 

1. Reproduce steps: Clearly, mention the steps to reproduce the bug.
2. Expected result: How the application should behave on the above-mentioned steps.
3. Actual result: What is the actual result of running the above steps i.e. the bug behavior.

dbDisconnect(con)

## [1] TRUE